AI insights into initial teacher education
Predictive models for student dropout
Bibliographic Data
| ID | 21496801 |
|---|---|
| Authors | Marcel Jud (0009-0005-1794-0255, University of Graz, corresponding author), Barbara Weissenbacher (0000-0003-2167-3960, University of Graz), Aljoscha C Neubauer (0000-0002-1326-3104, University of Graz) |
| Year | 2025 |
| Volume | 48 |
| Issue | 5 |
| Pages | 1123-1142 |
| Publication date | 2025-10-20 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | European Journal of Teacher Education (JOURNAL) |
| Journal identifiers | ISSN: 0261-9768 • E-ISSN: 1469-5928 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/02619768.2025.2535624 |
| OpenAlex | W4412684259 |
| Language | EN |
| Citations received | 1 |
| References cited | 61 |
Using stacked ensemble machine learning, we aim to predict the dropout of pre-service teachers. By training the data of 2661 students, we investigate the extent to which admission test data and demographic factors predict pre-service teachers’ dropout and how the integration of student success data can improve the accuracy of these predictions. We utilise an ensemble-model approach, consisting of three base models along with a meta-model, incorporating base model predictions to learn the strengths and weaknesses of base models, and therefore leveraging overall predictive performance. The results showed that 61% of students could be correctly classified (drop out/retention) using only admission test data and demographic information. When both admission test data and GPA were used, 76% of students were correctly classified. The model shows strong potential but also illustrates the need to develop more refined models that include historical data for early identification and intervention of at-risk students
Developmental psychology · Mathematics education · Pedagogy · Predictive validity · Student teacher · Teacher education · Computer Science · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics · Psychology · Reinforcement Learning in Robotics
An introduction to ROC analysis
Super Learner
Statistical Modeling
Testgüte und psychometrische Äquivalenz der deutschen Version des Big Five Inventory (BFI) bei jungen, mittelalten und alten Erwachsenen
The relationship between Precision-Recall and ROC curves
The Big Five and tertiary academic performance
Intelligence and school grades
Random Forests for Classification in Ecology
Emotional intelligence
Support-Vector Networks
Dropping out of university
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
A Meta-Analysis for Exploring the Diverse Causes and Effects of Stress in Teachers
The use of the area under the ROC curve in the evaluation of machine learning algorithms
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Should Teachers be Trained in Emergency Remote Teaching? Lessons Learned from the Covid-19 Pandemic
Stacked generalization
The Five-Factor Model of Personality and Its Relevance to Personality Disorders
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Dropout from Higher Education
Sample Selection Bias as a Specification Error
Academic and social integration and their relation to dropping out of teacher education
The relationship between teacher professional identity and burnout amid the pandemic
Cultivating Resilience and Success
What makes future teachers successful? A longitudinal study on pre-service teachers
Predictors of dropout intentions in teacher education programmes compared with other study programmes
Big Five personality traits and academic performance
Teachers’ life satisfaction
Factors that influence pre-service teachers' persistence
Dropout early warning systems for high school students using machine learning
A meta-analysis of the five-factor model of personality and academic performance
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |